weighted moving average

wma.py

def wma(values, window):
    
    # requires trinum.py
    
    # using definition provided at
    # http://www.oanda.com/forex-trading/learn/forex-indicators/weighted-moving-average
    
    # create an array of weights
    # use floats when creating array, or the result is integer division below
    # and, note that they are reversed.  why?  read this:
    # http://stackoverflow.com/questions/12816011/weighted-moving-average-with-numpy-convolve
    weights = np.arange(window, 0, -1.0)
    weights /= trinum(window)
    
    # created wma array with NaN values for indexes < window value
    weighted_moving_averages = np.empty(window-1)
    weighted_moving_averages[:] = np.NAN
    
    # then append the wma's onto the end
    weighted_moving_averages = np.append(weighted_moving_averages, np.convolve(values, weights, 'valid'))
    
    return weighted_moving_averages